Global health curricula in medical schools
Bibliographic record
Abstract
To meet the demand for increasing capacity of the global health (GH) workforce many medical schools worldwide are in the process of establishing GH curricula. Still, there is little consensus as to how to train future physicians with the skills, attitudes and knowledge required to meet the currents gaps in GH practice, policy, education, advocacy and research. Thus, the co-authors of this paper, all keenly interested and involved in achieving better GH education for medical schools, organized an open retreat to help address this. This paper summarizes the processes required and provides additional recommendations to fill this gap. Steps taken by the Medical School for International Health, a school which focuses on GH, and other schools and organizations (e.g., NOSM, GHEC, THEnet,) to establish GH competencies, education and training approaches, as well as outcome monitoring, and integration of teaching with communities, are reviewed. After guidelines were provided , we addressed topic areas important to GH medical education, such as competencies, planning methods of GH inoculation in curricula, GH clerkships, curricula monitoring and evaluation and principals of community interaction. We reviewed existing resources and processes in each area, identified gaps, noted barriers to implementation, and put forth recommendations for each topic area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".